New insights into the sources of atmospheric lead reaching the Arctic by isotopic analysis of PM10 atmospheric particles and resuspended soils
Bibliographic record
Abstract
Lead concentration, crustal enrichment factor, and isotope ratios (including those with the less abundant 204Pb) have been measured in PM10 samples collected at Ny-Ålesund (Svalbard Islands) from October 2018 to November 2020, including, for the first time, the autumn and winter seasons. In addition, resuspended soil samples from Svalbard Islands, Iceland, and Alaska were prepared and analysed to provide reference values for local and short-range potential source areas, and back-trajectory analysis was applied to corroborate the findings based on the Pb isotopic signatures. Results showed that the atmospheric Pb concentration reached its maximum in January, progressively decreased until August, remained low until November, and increased again in December. The Pb isotopic composition also showed a clear temporal variation, indicating a shift in the Pb sources with the changing seasons, in agreement with the back-trajectory analysis results. Lead in PM10 samples collected from November to May was entirely (>99%) anthropogenic, and it likely derived from the mining activities in East Kazakhstan and the Altai region, specifically at Leninogorsk (now Ridder). In contrast, the Pb isotopic composition of particulate samples collected from June to October reflected mixed anthropogenic contributions from the North-East USA and Canada, with a significant contribution from natural sources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".